The initial deep learning approaches involved scan-level classification to identify the suspicious baggage content [1]}. However, with the recent advancements in object detection, researchers also employed sophisticated detectors like RetinaNet [2]}, YOLO [3]}, [4]}, and Faster R-CNN [5]} to not only recognize the contraband items from the baggage X-ray scans but also to localize them via bounding boxes [6]}. Moreover, researchers also proposed semantic segmentation [7]}, [8]}, [9]}, [10]}, [11]} and instance segmentation [12]} models to recognize threatening and smuggled items from the grayscale and colored X-ray imagery. Apart from this, Xiao et al. [13]} presented an efficient implementation of Faster R-CNN [5]} to detect suspicious data from the TeraHertz imagery. Dhiraj et al. [15]} used Faster R-CNN [5]}, YOLOv2 [4]}, and Tiny YOLO [4]} to screen baggage threats contained within the scans of a publicly available GDXray dataset [19]}. Gaus et al. [20]} utilized RetinaNet [2]}, Faster R-CNN [5]}, Mask R-CNN [23]} (driven through ResNets [24]}, VGG-16 [25]}, and SqueezeNet [26]}) to detect prohibited baggage items. In another approach [27]}, they analyzed the transferability of these models on a similarly styled X-ray imagery contained within their local dataset as well as the SIXray10 subset of the publicly available SIXray dataset [28]}. Similarly, Akçay et al. [6]} compared Faster R-CNN [5]}, YOLOv2 [4]}, R-FCN [32]}, and sliding-window CNN with the AlexNet [33]} driven SVM model to recognize occluded contraband items from the X-ray imagery. Miao et al. [28]} explored the imbalanced nature of the contraband items in the real-world by developing a class-balanced hierarchical refinement (CHR) framework. Furthermore, they extensively tested their framework (backboned through different classification models) on their publicly released SIXray [28]} dataset. Wei et al. [36]} presented a plug-and-play module dubbed De-occlusion Attention Module (DOAM) that can be coupled with any object detector to enhance its capacity towards screening occluded contraband items. DOAM was validated on the publicly available OPIXray [36]} dataset, which is the first of its kind in providing quantitative assessments of baggage screening frameworks under low, partial, and full occlusion [36]}. 
Apart from this, Hassan et al. [39]} also addressed the imbalanced nature of the contraband data by developing the cascaded structure tensors (CST) based baggage threat detector. CST [39]} generates a balanced set of contour-based proposals, which are then utilized in training the backbone model to screen the normal and abnormal baggage items within the candidate scan [39]}. Similarly, to overcome the need to train the threat detection systems on large-scale and well-annotated data, Hassan et al. [42]} introduced meta-transfer learning-based dual tensor-shot (DTS) detector. DTS [42]} analyzes the scan's saliency to produce low and high-density contour maps from which the suspicious contraband items are identified effectively with few-shot training [42]}. In another approach, Hassan et al. [12]} developed an instance segmentation-based threat detection framework that filters the contours of the suspicious items from the regular content via trainable structure tensors (TST) [12]} to identify them accurately within the security X-ray imagery.

